{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1.2, 1.5, 1.8], [1.3, 1.4, 1.9],\n",
    "[1.1, 1.6, 1.7]]) \n",
    "y = np.array([5, 10, 9]).T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.2, 1.5, 1.8],\n",
       "       [1.3, 1.4, 1.9],\n",
       "       [1.1, 1.6, 1.7]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[1.2, 1.5, 1.8], [1.3, 1.4, 1.9],\n",
    "[1.1, 1.6, 1.7]]) \n",
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 10,  9])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37.2, 37.6, 36.8])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.dot(X,y)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.2, 1.5, 1.8], [1.3, 1.4, 1.9], [1.1, 1.6, 1.7]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [[1.2, 1.5, 1.8], [1.3, 1.4, 1.9],\n",
    "[1.1, 1.6, 1.7]]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 10, 9]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = [5, 10, 9]\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第二题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 6, 1, 1, 2, 8, 7, 3, 5, 6, 3, 5, 3, 5, 8, 8, 2, 8, 1, 7, 8,\n",
       "       7, 2, 1, 2, 9, 9, 4, 9])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "X = np.random.randint(1, 10, size=30)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9, 6],\n",
       "       [1, 1, 2],\n",
       "       [8, 7, 3],\n",
       "       [5, 6, 3],\n",
       "       [5, 3, 5],\n",
       "       [8, 8, 2],\n",
       "       [8, 1, 7],\n",
       "       [8, 7, 2],\n",
       "       [1, 2, 9],\n",
       "       [9, 4, 9]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = X.reshape(-1,3)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 2, 3, 3, 5, 2, 7, 2, 9, 9])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a[:,2]\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "cannot assign to comparison (<ipython-input-49-6fcd77be1429>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-49-6fcd77be1429>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    i <=3, i =0\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m cannot assign to comparison\n"
     ]
    }
   ],
   "source": [
    "for i in b:\n",
    "    i <=3, i =0\n",
    "    3 <= i<=6, i=1\n",
    "    i>=6,i=1\n",
    "print (b)\n",
    "##不会。。。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9, 6],\n",
       "       [1, 1, 2],\n",
       "       [8, 7, 3],\n",
       "       [5, 6, 3],\n",
       "       [5, 3, 5],\n",
       "       [8, 8, 2],\n",
       "       [8, 1, 7],\n",
       "       [8, 7, 2],\n",
       "       [1, 2, 9],\n",
       "       [9, 4, 9]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9],\n",
       "       [1, 1],\n",
       "       [8, 7],\n",
       "       [5, 6],\n",
       "       [5, 3],\n",
       "       [8, 8],\n",
       "       [8, 1],\n",
       "       [8, 7],\n",
       "       [1, 2],\n",
       "       [9, 4]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = a[:,0:2]\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 2, 3, 3, 5, 2, 7, 2, 9, 9])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = a[:,2]\n",
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第三题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/zhoukelly/Desktop/log.txt',header = None, sep = '\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns =['id', 'api', 'count', 'res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg', 'interval','created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91371</th>\n",
       "      <td>6897707</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>576.57</td>\n",
       "      <td>93.81</td>\n",
       "      <td>346.79</td>\n",
       "      <td>192.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-17 14:51:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32855</th>\n",
       "      <td>3023481</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>207.33</td>\n",
       "      <td>207.33</td>\n",
       "      <td>207.33</td>\n",
       "      <td>207.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-09 12:39:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163999</th>\n",
       "      <td>12243351</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>2476.04</td>\n",
       "      <td>90.25</td>\n",
       "      <td>502.41</td>\n",
       "      <td>247.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-13 15:22:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81832</th>\n",
       "      <td>6296130</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>15</td>\n",
       "      <td>2335.09</td>\n",
       "      <td>84.66</td>\n",
       "      <td>215.98</td>\n",
       "      <td>155.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-04 15:08:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15520</th>\n",
       "      <td>1545533</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>650.34</td>\n",
       "      <td>122.82</td>\n",
       "      <td>225.97</td>\n",
       "      <td>162.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-18 23:50:42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "91371    6897707  /front-api/bill/create      3        576.57         93.81   \n",
       "32855    3023481  /front-api/bill/create      1        207.33        207.33   \n",
       "163999  12243351  /front-api/bill/create     10       2476.04         90.25   \n",
       "81832    6296130  /front-api/bill/create     15       2335.09         84.66   \n",
       "15520    1545533  /front-api/bill/create      4        650.34        122.82   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "91371         346.79         192.0        60  2019-02-17 14:51:20  \n",
       "32855         207.33         207.0        60  2018-12-09 12:39:23  \n",
       "163999        502.41         247.0        60  2019-05-13 15:22:03  \n",
       "81832         215.98         155.0        60  2019-02-04 15:08:56  \n",
       "15520         225.97         162.0        60  2018-11-18 23:50:42  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5) ##随机采样，判断是否有重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.794960e+05</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.877739e+06</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.012494e+06</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.626440e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.825233e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.811510e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.981455e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.019163e+09</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          count   res_time_sum   res_time_min  \\\n",
       "count  1.794960e+05  179496.000000  179496.000000  179496.000000   \n",
       "mean   6.877739e+06       7.175909    1393.177832     108.419626   \n",
       "std    6.012494e+06       4.325160    1499.486073      79.640693   \n",
       "min    1.626440e+05       1.000000      36.550000       3.210000   \n",
       "25%    3.825233e+06       4.000000     607.707500      83.410000   \n",
       "50%    6.811510e+06       7.000000    1154.905000      97.120000   \n",
       "75%    9.981455e+06      10.000000    1834.117500     116.990000   \n",
       "max    2.019163e+09      31.000000  142650.550000   18896.640000   \n",
       "\n",
       "        res_time_max   res_time_avg  interval  \n",
       "count  179496.000000  179496.000000  179496.0  \n",
       "mean      359.880374     187.812208      60.0  \n",
       "std       638.919827     224.464813       0.0  \n",
       "min        36.550000      36.000000      60.0  \n",
       "25%       198.280000     144.000000      60.0  \n",
       "50%       256.090000     167.000000      60.0  \n",
       "75%       374.410000     202.000000      60.0  \n",
       "max    142468.270000   71325.000000      60.0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() ##查看数据属否有异常"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['interval'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('interval', axis=1) ##删除 API和interval，优化内存（API那行手误删除了，怎么回复呢？）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['created_at'] ## 将created_at作为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df['2019-5-1'].resample('1H').mean()  ##对一天的数据重新采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>1.140832e+07</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>1153.457321</td>\n",
       "      <td>145.571071</td>\n",
       "      <td>517.395357</td>\n",
       "      <td>264.321429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>1.141199e+07</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>453.751818</td>\n",
       "      <td>156.766364</td>\n",
       "      <td>238.241136</td>\n",
       "      <td>190.159091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.141438e+07</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>387.049167</td>\n",
       "      <td>195.488333</td>\n",
       "      <td>247.685833</td>\n",
       "      <td>214.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>1.141665e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>221.170000</td>\n",
       "      <td>81.620000</td>\n",
       "      <td>139.550000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.141855e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>349.500000</td>\n",
       "      <td>349.500000</td>\n",
       "      <td>349.500000</td>\n",
       "      <td>349.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.141986e+07</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>274.545000</td>\n",
       "      <td>211.272000</td>\n",
       "      <td>227.675000</td>\n",
       "      <td>219.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.142202e+07</td>\n",
       "      <td>1.604651</td>\n",
       "      <td>228.196744</td>\n",
       "      <td>134.999070</td>\n",
       "      <td>156.486512</td>\n",
       "      <td>145.953488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>1.142519e+07</td>\n",
       "      <td>3.298246</td>\n",
       "      <td>735.070526</td>\n",
       "      <td>136.952982</td>\n",
       "      <td>327.808596</td>\n",
       "      <td>209.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>1.142893e+07</td>\n",
       "      <td>6.866667</td>\n",
       "      <td>1470.257000</td>\n",
       "      <td>109.189500</td>\n",
       "      <td>466.986500</td>\n",
       "      <td>208.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>1.143322e+07</td>\n",
       "      <td>10.483333</td>\n",
       "      <td>2680.561000</td>\n",
       "      <td>88.963333</td>\n",
       "      <td>700.043500</td>\n",
       "      <td>246.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>1.143768e+07</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>2710.358500</td>\n",
       "      <td>87.740500</td>\n",
       "      <td>656.273500</td>\n",
       "      <td>219.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>1.144241e+07</td>\n",
       "      <td>9.916667</td>\n",
       "      <td>2170.651167</td>\n",
       "      <td>89.900667</td>\n",
       "      <td>551.808500</td>\n",
       "      <td>222.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>1.144698e+07</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>1492.626667</td>\n",
       "      <td>92.688167</td>\n",
       "      <td>425.888500</td>\n",
       "      <td>188.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>1.145130e+07</td>\n",
       "      <td>6.783333</td>\n",
       "      <td>1930.566000</td>\n",
       "      <td>114.959333</td>\n",
       "      <td>602.973333</td>\n",
       "      <td>267.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>1.145562e+07</td>\n",
       "      <td>9.850000</td>\n",
       "      <td>2610.670167</td>\n",
       "      <td>100.781167</td>\n",
       "      <td>666.500833</td>\n",
       "      <td>260.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>1.146011e+07</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2842.611500</td>\n",
       "      <td>91.434500</td>\n",
       "      <td>716.380500</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>1.146471e+07</td>\n",
       "      <td>10.416667</td>\n",
       "      <td>2688.735167</td>\n",
       "      <td>96.285667</td>\n",
       "      <td>771.891167</td>\n",
       "      <td>252.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>1.146933e+07</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1954.618833</td>\n",
       "      <td>106.972667</td>\n",
       "      <td>566.571333</td>\n",
       "      <td>238.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>1.147364e+07</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>1008.322333</td>\n",
       "      <td>110.131833</td>\n",
       "      <td>374.192000</td>\n",
       "      <td>199.950000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               id      count  res_time_sum  res_time_min  \\\n",
       "created_at                                                                 \n",
       "2019-05-01 00:00:00  1.140832e+07   4.428571   1153.457321    145.571071   \n",
       "2019-05-01 01:00:00  1.141199e+07   2.272727    453.751818    156.766364   \n",
       "2019-05-01 02:00:00  1.141438e+07   1.833333    387.049167    195.488333   \n",
       "2019-05-01 03:00:00           NaN        NaN           NaN           NaN   \n",
       "2019-05-01 04:00:00           NaN        NaN           NaN           NaN   \n",
       "2019-05-01 05:00:00  1.141665e+07   2.000000    221.170000     81.620000   \n",
       "2019-05-01 06:00:00           NaN        NaN           NaN           NaN   \n",
       "2019-05-01 07:00:00           NaN        NaN           NaN           NaN   \n",
       "2019-05-01 08:00:00           NaN        NaN           NaN           NaN   \n",
       "2019-05-01 09:00:00  1.141855e+07   1.000000    349.500000    349.500000   \n",
       "2019-05-01 10:00:00  1.141986e+07   1.400000    274.545000    211.272000   \n",
       "2019-05-01 11:00:00  1.142202e+07   1.604651    228.196744    134.999070   \n",
       "2019-05-01 12:00:00  1.142519e+07   3.298246    735.070526    136.952982   \n",
       "2019-05-01 13:00:00  1.142893e+07   6.866667   1470.257000    109.189500   \n",
       "2019-05-01 14:00:00  1.143322e+07  10.483333   2680.561000     88.963333   \n",
       "2019-05-01 15:00:00  1.143768e+07  12.333333   2710.358500     87.740500   \n",
       "2019-05-01 16:00:00  1.144241e+07   9.916667   2170.651167     89.900667   \n",
       "2019-05-01 17:00:00  1.144698e+07   7.666667   1492.626667     92.688167   \n",
       "2019-05-01 18:00:00  1.145130e+07   6.783333   1930.566000    114.959333   \n",
       "2019-05-01 19:00:00  1.145562e+07   9.850000   2610.670167    100.781167   \n",
       "2019-05-01 20:00:00  1.146011e+07  11.000000   2842.611500     91.434500   \n",
       "2019-05-01 21:00:00  1.146471e+07  10.416667   2688.735167     96.285667   \n",
       "2019-05-01 22:00:00  1.146933e+07   8.000000   1954.618833    106.972667   \n",
       "2019-05-01 23:00:00  1.147364e+07   5.083333   1008.322333    110.131833   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2019-05-01 00:00:00    517.395357    264.321429  \n",
       "2019-05-01 01:00:00    238.241136    190.159091  \n",
       "2019-05-01 02:00:00    247.685833    214.083333  \n",
       "2019-05-01 03:00:00           NaN           NaN  \n",
       "2019-05-01 04:00:00           NaN           NaN  \n",
       "2019-05-01 05:00:00    139.550000    110.000000  \n",
       "2019-05-01 06:00:00           NaN           NaN  \n",
       "2019-05-01 07:00:00           NaN           NaN  \n",
       "2019-05-01 08:00:00           NaN           NaN  \n",
       "2019-05-01 09:00:00    349.500000    349.000000  \n",
       "2019-05-01 10:00:00    227.675000    219.700000  \n",
       "2019-05-01 11:00:00    156.486512    145.953488  \n",
       "2019-05-01 12:00:00    327.808596    209.210526  \n",
       "2019-05-01 13:00:00    466.986500    208.916667  \n",
       "2019-05-01 14:00:00    700.043500    246.283333  \n",
       "2019-05-01 15:00:00    656.273500    219.150000  \n",
       "2019-05-01 16:00:00    551.808500    222.616667  \n",
       "2019-05-01 17:00:00    425.888500    188.016667  \n",
       "2019-05-01 18:00:00    602.973333    267.150000  \n",
       "2019-05-01 19:00:00    666.500833    260.600000  \n",
       "2019-05-01 20:00:00    716.380500    251.000000  \n",
       "2019-05-01 21:00:00    771.891167    252.183333  \n",
       "2019-05-01 22:00:00    566.571333    238.583333  \n",
       "2019-05-01 23:00:00    374.192000    199.950000  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2[['res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120e45970>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1' : '2019-5-10']['count'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "\n",
       "                     res_time_max  res_time_avg           created_at  weekday  \n",
       "created_at                                                                     \n",
       "2018-11-01 00:00:07        177.72         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07        240.38         149.0  2018-11-01 00:01:07        3  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday'] = df.index.weekday\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg           created_at  weekday  \\\n",
       "created_at                                                                      \n",
       "2018-11-01 00:00:07        177.72         132.0  2018-11-01 00:00:07        3   \n",
       "2018-11-01 00:01:07        240.38         149.0  2018-11-01 00:01:07        3   \n",
       "2018-11-01 00:02:07        225.73         169.0  2018-11-01 00:02:07        3   \n",
       "2018-11-01 00:03:07        196.61         145.0  2018-11-01 00:03:07        3   \n",
       "2018-11-01 00:04:07        232.02         189.0  2018-11-01 00:04:07        3   \n",
       "\n",
       "                     weekend  \n",
       "created_at                    \n",
       "2018-11-01 00:00:07    False  \n",
       "2018-11-01 00:01:07    False  \n",
       "2018-11-01 00:02:07    False  \n",
       "2018-11-01 00:03:07    False  \n",
       "2018-11-01 00:04:07    False  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['weekday'].isin({5,6})\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x128b84940>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean().unstack(level=0).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
